Strong analytics: SAS and Teradata launch relational database partnership

A partnership of two former competitors brings together the respective strengths of both companies in business analytics and enterprise data warehousing.
By Manufacturing Business Technology Staff October 8, 2007

Business intelligence (BI) and analytical software and services provider SAS is teaming up with Teradata Corp ., a specialist in enterprise data warehousing, for deeper technical integration of their respective products.

‘This partnership of two former competitors brings together the respective strengths of both companies in business analytics and enterprise data warehousing,’ says Dan Vesset, VP, Business Analytics, IDC . ‘In-database analytics promises decrease data movement and increase performance, thus enabling IT to better respond to the decision-support needs of business [leaders].’

Following up on the recently launched SAS In-Database initiative, the SAS-Teradata partnership will enable businesses to run and optimize key aspects of SAS solutions and analytic processes within the Teradata database engine. Customers can leverage SAS capabilities and analytical functions to utilize the core parallel processing inherent in Teradata’s architecture.

‘As data volumes grow exponentially, leading companies are making major investments in analytic solutions across the enterprise—from customer information, marketing, supply chain, and risk, to finance, IT, and operations,’ says Jim Goodnight, CEO, SAS. ‘[This collaboration with] Teradata marks our first relational database partnership—one that will open up new opportunities for existing and future customers interested in utilizing the power of SAS solutions and analytics within Teradata’s database engine.’

Teradata’s ability to aggregate, parse, and sort data—as well as handle large data sets—provides well-matched environment for the data integration, business intelligence, and analytics capabilities of SAS. These technologies are the underpinnings for analytic solutions focused on specific business issues. For example, manufacturers will be able to more quickly pinpoint problems to prevent costly product recalls.